Python keras.utils.conv_utils.normalize_data_format() Examples

The following are 25 code examples of keras.utils.conv_utils.normalize_data_format(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.utils.conv_utils , or try the search function .
Example #1
Source File: interpolate.py    From CRAFT_keras with Apache License 2.0 6 votes vote down vote up 
def __init__(self, target_layer, data_format=None, **kwargs):
        """
        :param target_layer: Tensor or variable. Resize the images to the same size as it is.
        :param data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        :param kwargs:
        """
        super(Interpolate, self).__init__(**kwargs)
        self.target_layer = target_layer
        self.target_shape = _collect_input_shape(target_layer)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=4)
Example #2
Source File: layers.py    From keras-fcn with MIT License 6 votes vote down vote up 
def __init__(self, target_shape, offset=None, data_format=None,
                 **kwargs):
        """Crop to target.

        If only one `offset` is set, then all dimensions are offset by this amount.

        """
        super(CroppingLike2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.target_shape = target_shape
        if offset is None or offset == 'centered':
            self.offset = 'centered'
        elif isinstance(offset, int):
            self.offset = (offset, offset)
        elif hasattr(offset, '__len__'):
            if len(offset) != 2:
                raise ValueError('`offset` should have two elements. '
                                 'Found: ' + str(offset))
            self.offset = offset
        self.input_spec = InputSpec(ndim=4)
Example #3
Source File: layer_utils.py    From deep_learning with MIT License 6 votes vote down vote up 
def __init__(self, padding=(1, 1), data_format=None, **kwargs):
        super(ReflectionPadding2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(padding, int):
            self.padding = ((padding, padding), (padding, padding))
        elif hasattr(padding,"__len__"):
            if len(padding) != 2:
                 raise ValueError('`padding` should have two elements. '
                                  'Found: ' + str(padding))
            height_padding = conv_utils.normalize_tuple(padding[0], 2, "1st entry of padding")
            width_padding = conv_utils.normalize_tuple(padding[1], 2, "2nd entry of padding")
            self.padding = (height_padding, width_padding)
        else:
            raise ValueError('`padding` should be either an int, '
                             'a tuple of 2 ints '
                             '(symmetric_height_pad, symmetric_width_pad), '
                             'or a tuple of 2 tuples of 2 ints '
                             '((top_pad, bottom_pad), (left_pad, right_pad)). '
                             'Found: ' + str(padding))
            self.input_spec = InputSpec(ndim=4)
Example #4
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #5
Source File: subpixel_upscaling.py    From SegCaps with Apache License 2.0 5 votes vote down vote up 
def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = normalize_data_format(data_format)
Example #6
Source File: subpixel.py    From SSR-Net with Apache License 2.0 5 votes vote down vote up 
def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = normalize_data_format(data_format)
Example #7
Source File: subpixel.py    From PyTorch-Luna16 with Apache License 2.0 5 votes vote down vote up 
def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = normalize_data_format(data_format)
Example #8
Source File: capslayers.py    From deepcaps with MIT License 5 votes vote down vote up 
def __init__(self, ch_j, n_j,
                 kernel_size=(3, 3),
                 strides=(1, 1),
                 r_num=1,
                 b_alphas=[8, 8, 8],
                 padding='same',
                 data_format='channels_last',
                 dilation_rate=(1, 1),
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 **kwargs):
        super(Conv2DCaps, self).__init__(**kwargs)
        rank = 2
        self.ch_j = ch_j  # Number of capsules in layer J
        self.n_j = n_j  # Number of neurons in a capsule in J
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.r_num = r_num
        self.b_alphas = b_alphas
        self.padding = conv_utils.normalize_padding(padding)
        #self.data_format = conv_utils.normalize_data_format(data_format)
        self.data_format = K.normalize_data_format(data_format)
        self.dilation_rate = (1, 1)
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.input_spec = InputSpec(ndim=rank + 3)
Example #9
Source File: layers.py    From nn_playground with MIT License 5 votes vote down vote up 
def __init__(self, 
                 ratio, 
                 data_format=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(SE, self).__init__(**kwargs)

        self.ratio = ratio
        self.data_format= conv_utils.normalize_data_format(data_format)

        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.supports_masking = True
Example #10
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #11
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #12
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #13
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #14
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #15
Source File: pixel_shuffler.py    From df with Mozilla Public License 2.0 5 votes vote down vote up 
def __init__(self, size=(2, 2), data_format=None, **kwargs):
        super(PixelShuffler, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 2, 'size')
Example #16
Source File: conv_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_invalid_data_format():
    with pytest.raises(ValueError):
        conv_utils.normalize_data_format('channels_middle')
Example #17
Source File: models.py    From pOSAL with MIT License 5 votes vote down vote up 
def __init__(self, upsampling=(2, 2), output_size=None, data_format=None, **kwargs):

        super(BilinearUpsampling, self).__init__(**kwargs)

        self.data_format = conv_utils.normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=4)
        if output_size:
            self.output_size = conv_utils.normalize_tuple(
                output_size, 2, 'output_size')
            self.upsampling = None
        else:
            self.output_size = None
            self.upsampling = conv_utils.normalize_tuple(
                upsampling, 2, 'upsampling')
Example #18
Source File: subpixel.py    From semantic-embeddings with MIT License 5 votes vote down vote up 
def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = normalize_data_format(data_format)
Example #19
Source File: cifar_resnet.py    From semantic-embeddings with MIT License 5 votes vote down vote up 
def __init__(self, padding=1, data_format=None, **kwargs):
        super(ChannelPadding, self).__init__(**kwargs)
        self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
        self.data_format = normalize_data_format(data_format)
        self.input_spec = InputSpec(ndim=4)
Example #20
Source File: subpixel.py    From Model-Playgrounds with MIT License 5 votes vote down vote up 
def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = normalize_data_format(data_format)
Example #21
Source File: sn.py    From Coloring-greyscale-images with MIT License 5 votes vote down vote up 
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 spectral_normalization=True,
                 **kwargs):
        super(_ConvSN, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=self.rank + 2)
        self.spectral_normalization = spectral_normalization
        self.u = None
Example #22
Source File: pixel_shuffler.py    From youtube-video-face-swap with MIT License 5 votes vote down vote up 
def __init__(self, size=(2, 2), data_format=None, **kwargs):
        super(PixelShuffler, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 2, 'size')
Example #23
Source File: pspnet.py    From keras-image-segmentation with MIT License 5 votes vote down vote up 
def __init__(self, target_shape, offset=None, data_format=None, **kwargs):
        super(CroppingLike2D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.target_shape = target_shape
        if offset is None or offset == 'centered':
            self.offset = 'centered'
        elif isinstance(offset, int):
            self.offset = (offset, offset)
        elif hasattr(offset, '__len__'):
            if len(offset) != 2:
                raise ValueError('`offset` should have two elements. '
                                 'Found: ' + str(offset))
            self.offset = offset
        self.input_spec = InputSpec(ndim=4)
Example #24
Source File: exampleTrainer.py    From df with Mozilla Public License 2.0 5 votes vote down vote up 
def __init__(self, size=(2, 2), data_format=None, **kwargs):
        super(PixelShuffler, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.size = conv_utils.normalize_tuple(size, 2, 'size')
Example #25
Source File: conv.py    From deep_complex_networks with MIT License 4 votes vote down vote up 
def __init__(self, rank,
                 filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 dilation_rate=1,
                 activation=None,
                 use_bias=True,
                 normalize_weight=False,
                 kernel_initializer='complex',
                 bias_initializer='zeros',
                 gamma_diag_initializer=sqrt_init,
                 gamma_off_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 gamma_diag_regularizer=None,
                 gamma_off_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 gamma_diag_constraint=None,
                 gamma_off_constraint=None,
                 init_criterion='he',
                 seed=None,
                 spectral_parametrization=False,
                 epsilon=1e-7,
                 **kwargs):
        super(ComplexConv, self).__init__(**kwargs)
        self.rank = rank
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        self.data_format = 'channels_last' if rank == 1 else conv_utils.normalize_data_format(data_format)
        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.normalize_weight = normalize_weight
        self.init_criterion = init_criterion
        self.spectral_parametrization = spectral_parametrization
        self.epsilon = epsilon
        self.kernel_initializer = sanitizedInitGet(kernel_initializer)
        self.bias_initializer = sanitizedInitGet(bias_initializer)
        self.gamma_diag_initializer = sanitizedInitGet(gamma_diag_initializer)
        self.gamma_off_initializer = sanitizedInitGet(gamma_off_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.gamma_diag_regularizer = regularizers.get(gamma_diag_regularizer)
        self.gamma_off_regularizer = regularizers.get(gamma_off_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.gamma_diag_constraint = constraints.get(gamma_diag_constraint)
        self.gamma_off_constraint = constraints.get(gamma_off_constraint)
        if seed is None:
            self.seed = np.random.randint(1, 10e6)
        else:
            self.seed = seed
        self.input_spec = InputSpec(ndim=self.rank + 2)